The present invention relates generally to multiple input, multiple output (MIMO) communication systems and in particular to soft symbol decoders for MIMO communication systems with reduced search complexity.
MIMO communication systems are known in the art. Such systems generally include a transmitter having a number (Mt) of transmit antennas communicating with a receiver having a number (Mr) of receive antennas, where Mr and Mt may or may not be equal. In some keying schemes, bits of data to be transmitted are grouped and each group of bits is mapped to a symbol (a particular combination of phase and amplitude) in a signaling constellation. A number of constellations are known in the art, including binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), and quadrature amplitude modulation (QAM) constellations. In a MIMO communication system, each of the Mt transmit antenna transmits, at substantially the same time, a symbol representing a different group of bits. Thus, if each symbol represents B bits, the number of bits transmitted per channel “period” is B*Mt.
Each receive antenna receives a signal that is a combination of signals from the transmit antennas, modified by channel properties (e.g., fading and delay) and noise. The receiver decodes (i.e., reconstructs) the Mt transmitted signals from the Mr received signals using its knowledge of the possible transmitted symbols and the properties of the communication channel. In some existing systems, decoding involves a “soft symbol” decoding phase and a “hard symbol” decoding phase. In the soft symbol decoding phase, possible combinations of transmitted symbols, after accounting for channel effects and noise, are compared to the received signal to generate a soft metric for each of the B*Mt transmitted bits. The soft metric is a numerical value representing the relative likelihood that the transmitted bit had a value of 1 (or 0). In the hard symbol decoding phase, the soft metrics are used to generate an output data stream using algorithms such as the well-known Viterbi algorithm. Ideally, the output data stream is identical to the input data stream. In practice, there is a non-zero bit error rate that depends on a number of factors.
In conventional MIMO systems, soft metrics are generated by searching over each possible combination of constellation points. If the number of constellation points for one transmitter is C, the order, or complexity, of such a search is CMt. For example, in a MIMO system with two transmit antennas using a 16QAM (16-point quadrature amplitude modulation) constellation, the search order is 162=256. Larger constellations and larger numbers of transmit antennas further increase the search complexity. More complex searches require more powerful processors in the receiver to keep up with the incoming data rate, which can increase the cost of the communication system.
To the extent that search complexity can be reduced, receiver architecture can be simplified, with attendant cost savings. Some examples of reduced-complexity searches are described in the above-referenced co-pending U.S. patent application Ser. No. 10/068,571. The searches described therein reduce the search complexity to 2CMt−1.
It would be desirable to provide reduced complexity search algorithms for decoders that further reduce the search complexity without unacceptably increasing the bit error rate.